Fetching the paper…
Reading the bibliography…
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs.
Making bertha drive—an autonomous journey on a historic route
J. Ziegler, P. Bender, M. Schreiber, H. Lategahn, T. Strauss, C. Stiller, T. Dang, U. Franke, N. Appenrodt, C. G. Keller, E. Kaus, R. G. Herrtwich, C. Rabe, D. Pfeiffer, F. Lindner, F. Stein, F. Erbs, M. Enzweiler, C. Knöppel, J. Hipp, M. Haueis, M. Trepte, C. Brenk, A. Tamke, M. Ghanaat, M. Braun, A. Joos, H. Fritz, H. Mock, M. Hein, and E. Zeeb · 1902
Earlier work this paper cites.
Innovative artificial fog production device-a technical facility for research activities
M. Colomb, J. Dufour, M. Hirech, P. Lacôte, P. Morange, and J.-J. Boreux · 2004
Earlier work this paper cites.
Fog definition
U. D. of Commerce / National Oceanic and A. Administration · 2005
Earlier work this paper cites.
Segmentation and recognition using structure from motion point clouds
G. J. Brostow, J. Shotton, J. Fauqueur, and R. Cipolla · 2008
Earlier work this paper cites.
Laser gated camera imaging system and method, may 2008
S. Inbar and O. David · 2008
Earlier work this paper cites.
Lidar and vision-based pedestrian detection system
C. Premebida, O. Ludwig, and U. Nunes · 2009
Earlier work this paper cites.
Path planning for autonomous vehicles in unknown semi-structured environments
D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel · 2010
Earlier work this paper cites.
Improved visibility of road scene images under heterogeneous fog
J.-P. Tarel, N. Hautiere, A. Cord, D. Gruyer, and H. Halmaoui · 2010
Earlier work this paper cites.
On the roles of circulation and aerosols in the decline of mist and dense fog in europe over the last 30 years
G. J. van Oldenborgh, P. Yiou, and R. Vautard · 2010
Earlier work this paper cites.
Single image haze removal using dark channel prior
K. He, J. Sun, and X. Tang · 2011
Earlier work this paper cites.
Pedestrian detection: An evaluation of the state of the art
P. Dollar, C. Wojek, B. Schiele, and P. Perona · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Earlier work this paper cites.
A multi-sensor fusion system for moving object detection and tracking in urban driving environments
H. Cho, Y.-W. Seo, B. V. Kumar, and R. R. Rajkumar · 2014
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Active gated imaging in driver assistance system
Y. Grauer · 2014
Earlier work this paper cites.
Recent progress in road and lane detection: a survey
A. B. Hillel, R. Lerner, D. Levi, and G. Raz · 2014
Earlier work this paper cites.
Continuous manifold based adaptation for evolving visual domains
J. Hoffman, T. Darrell, and K. Saenko · 2014
Earlier work this paper cites.
Single image dehazing with image entropy and information fidelity
D. Park, H. Park, D. K. Han, and H. Ko · 2014
Earlier work this paper cites.
Fast fog detection for camera based advanced driver assistance systems
R. Spinneker, C. Koch, S. Park, and J. J. Yoon · 2014
Earlier work this paper cites.
Fast r-cnn
R. Girshick · 2015
Earlier work this paper cites.
Review of pedestrian detection techniques in automotive far-infrared video
P. Hurney, P. Waldron, F. Morgan, E. Jones, and M. Glavin · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
End to end learning for self-driving cars
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016
Earlier work this paper cites.
DehazeNet: An end-to-end system for single image haze removal
B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao · 2016
Earlier work this paper cites.
A unified multi-scale deep convolutional neural network for fast object detection
Z. Cai, Q. Fan, R. S. Feris, and N. Vasconcelos · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
Cited alongside, same era.
Methodology used to evaluate computer vision algorithms in adverse weather conditions
P. Duthon, F. Bernardin, F. Chausse, and M. Colomb · 2016
Cited alongside, same era.
Test methodology for rain influence on automotive surround sensors
S. Hasirlioglu, A. Kamann, I. Doric, and T. Brandmeier · 2016
Cited alongside, same era.
Vision for looking at traffic lights: Issues, survey, and perspectives
M. B. Jensen, M. P. Philipsen, A. Møgelmose, T. B. Moeslund, and M. M. Trivedi · 2016
Cited alongside, same era.
Learning depth from single monocular images using deep convolutional neural fields
Fully end-to-end learning based conditional boundary equilibrium gan with receptive field sizes enlarged for single ultra-high resolution image dehazing
S. Ki, H. Sim, J.-S. Choi, S. Kim, and M. Kim · 2018
Later among the works it cites.
Robust camera lidar sensor fusion via deep gated information fusion network
J. Kim, J. Choi, Y. Kim, J. Koh, C. C. Chung, and J. W. Choi · 2018
Later among the works it cites.
Joint 3d proposal generation and object detection from view aggregation
J. Ku, M. Mozifian, J. Lee, A. Harakeh, and S. L. Waslander · 2018
Later among the works it cites.
Deblurgan: Blind motion deblurring using conditional adversarial networks
O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas · 2018
Later among the works it cites.
Conditional adversarial domain adaptation
M. Long, Z. Cao, J. Wang, and M. I. Jordan · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
F. Liu, C. Shen, G. Lin, and I. D. Reid · 2016
Cited alongside, same era.
Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
Cited alongside, same era.
Choosing smartly: Adaptive multimodal fusion for object detection in changing environments
O. Mees, A. Eitel, and W. Burgard · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
A. Shrivastava, A. Gupta, and R. Girshick · 2016
Cited alongside, same era.
Deep sliding shapes for amodal 3d object detection in rgb-d images
S. Song and J. Xiao · 2016
Cited alongside, same era.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2017
Cited alongside, same era.
Multi-view 3d object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
Cited alongside, same era.
Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net
W. Luo, B. Yang, and R. Urtasun · 2018
Later among the works it cites.
Image to image translation for domain adaptation
Z. Murez, S. Kolouri, D. Kriegman, R. Ramamoorthi, and K. Kim · 2018
Later among the works it cites.
Frustum pointnets for 3d object detection from rgb-d data
C. R. Qi, W. Liu, C. Wu, H. Su, and L. J. Guibas · 2018
Later among the works it cites.
Semantic foggy scene understanding with synthetic data
C. Sakaridis, D. Dai, and L. Van Gool · 2018
Later among the works it cites.
High-resolution image dehazing with respect to training losses and receptive field sizes
H. Sim, S. Ki, J.-S. Choi, S. Seo, S. Kim, and M. Kim · 2018
Later among the works it cites.
High-resolution image synthesis and semantic manipulation with conditional gans
T.-C. Wang, M.-Y. Liu, J.-Y. Zhu, A. Tao, J. Kautz, and B. Catanzaro · 2018
Later among the works it cites.
Pointfusion: Deep sensor fusion for 3d bounding box estimation
D. Xu, D. Anguelov, and A. Jain · 2018
Later among the works it cites.
Pixor: Real-time 3d object detection from point clouds
B. Yang, W. Luo, and R. Urtasun · 2018
Later among the works it cites.
NTIRE 2018 Challenge on Image Dehazing : Methods and Results
M.-h. Yang, V. M. Patel, J.-s. Choi, S. Kim, B. Chanda, P. Wang, Y. Chen, A. Alvarez-gila, A. Galdran, J. Vazquez-corral, M. Bertalmo, H. S. Demir, and J. Chen · 2018
Later among the works it cites.
Bdd100k: A diverse driving video database with scalable annotation tooling
F. Yu, W. Xian, Y. Chen, F. Liu, M. Liao, V. Madhavan, and T. Darrell · 2018
Later among the works it cites.
Residual dense network for image super-resolution
Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu · 2018
Later among the works it cites.
Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
Later among the works it cites.
nuscenes: A multimodal dataset for autonomous driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom · 2019
Closest in time.
Argoverse: 3d tracking and forecasting with rich maps
M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays · 2019
Closest in time.
Gated2depth: Real-time dense lidar from gated images
T. Gruber, F. Julca-Aguilar, M. Bijelic, and F. Heide · 2019
Closest in time.
Guided curriculum model adaptation and uncertainty-aware evaluation for semantic nighttime image segmentation
C. Sakaridis, D. Dai, and L. V. Gool · 2019
Closest in time.
Mvx-net: Multimodal voxelnet for 3d object detection
V. A. Sindagi, Y. Zhou, and O. Tuzel · 2019
Closest in time.
Scalability in perception for autonomous driving: Waymo open dataset, 2019
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine, V. Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y. Zhang, J. Shlens, Z. Chen, and D. Anguelov · 2019
Closest in time.
Dada: Depth-aware domain adaptation in semantic segmentation
T.-H. Vu, H. Jain, M. Bucher, M. Cord, and P. Pérez · 2019
Closest in time.